Bridging the gap between artificial intelligence and clinical readiness in endometriosis diagnosis : a systematic review

Abstract

Objective: To systematically evaluate the methodological quality and diagnostic performance of artificial intelligence (AI) applications, specifically machine learning (ML) and deep learning (DL), in the diagnosis of endometriosis through imaging and clinical symptomology.

Data Sources: A systematic search was conducted across seven databases for studies published between 2015 and 2025

Method of Study Selection: Inclusion criteria focused on primary research utilizing AI for endometriosis diagnosis via MRI, ultrasound, or patient-reported symptoms. Methodological quality was appraised using the QUADAS-2 tool. Study selection adhered to a double-blinded protocol to minimize selection bias. Clinical and methodological conflicts were addressed by a Professor of Radiography, while technical AI complexities were adjudicated by a Professor of Artificial Intelligence.

Tabulations, Integration and Results: AI models demonstrated high technical efficacy, with imaging-based algorithms achieving diagnostic accuracies up to 94.32% (MRI) and AUCs of 0.90 (Ultrasound). Symptom-based models reported accuracies reaching 95.95%, utilizing classifiers such as Random Forest and XGBoost. However, quality appraisal revealed significant clinical heterogeneity and systemic vulnerabilities. Spectrum bias was prevalent, as most models were trained on advanced-stage cohorts, limiting applicability for early-stage detection. Furthermore, symptom-based models often relied on self-reported data from social media, introducing significant selection and verification bias.

Conclusion: While AI demonstrates high potential for automating endometriosis detection, current literature is constrained by retrospective designs and narrow patient selection. To move from experimental prototypes to clinical screening tools, future research must prioritize prospective validation in undifferentiated populations using a combination of diagnostic reference methods. Journal of Minimally Invasive Gynecology (2026) 00, 1−9. © 2026 Published by Elsevier Inc. on behalf of AAGL.